Methods and systems using observation based techniques for determining performance capacity of a resource of a networked storage environment

ABSTRACT

Methods and systems for managing resources in a networked storage environment are provided. One method includes generating a relationship between latency and utilization of a resource in a networked storage environment using observation based, current and historical latency and utilization data, where latency is an indicator of delay at the resource for processing any request and utilization of the resource is an indicator of an extent the resource is being used at any given time; and selecting an optimal point for the generated relationship between latency and utilization, where the optimal point is an indicator of resource utilization beyond which throughput gains for a workload is smaller than increase in latency.

TECHNICAL FIELD

The present disclosure relates to managing resources in a networkedstorage environment.

BACKGROUND

Various forms of storage systems are used today. These forms includedirect attached storage (DAS) network attached storage (NAS) systems,storage area networks (SANs), and others. Network storage systems arecommonly used for a variety of purposes, such as providing multipleclients with access to shared data, backing up data and others.

A storage system typically includes at least a computing systemexecuting a storage operating system for storing and retrieving data onbehalf of one or more client computing systems (may just be referred toas “client” or “clients”). The storage operating system stores andmanages shared data containers in a set of mass storage devices.

Quality of Service (QOS) is a metric used in a storage environment toprovide certain throughput for processing input/output (I/O) requestsfor reading or writing data, a response time goal within, which I/Orequests are processed and a number of I/O requests processed within agiven time (for example, in a second (IOPS). Throughput means amount ofdata transferred within a given time, for example, in megabytes persecond (Mb/s).

To process an I/O request to read and/or write data, various resourcesare used within a storage system, for example, processors at storagesystem nodes, storage devices and others. The different resourcesperform various functions in processing the I/O requests and have finitecapacity to process requests. As storage systems continue to expand insize, complexity and operating speeds, it is desirable to efficientlymonitor and manage resource usage and know what capacity of a resourcemay be available at any given time. Continuous efforts are being made tobetter manage resources of networked storage environments.

SUMMARY

In one aspect, a machine implemented method is provided. The methodincludes generating a relationship between latency and utilization of aresource in a networked storage environment using observation based,current and historical latency and utilization data, where latency is anindicator of delay at the resource for processing any request andutilization of the resource is an indicator of an extent the resource isbeing used at any given time; and selecting an optimal point for thegenerated relationship between latency and utilization, where theoptimal point is an indicator of resource utilization beyond whichthroughput gains for a workload is smaller than increase in latency.

In another aspect, a non-transitory, machine readable storage mediumhaving stored thereon instructions with machine executable code whichwhen executed by at least one machine, causes the machine to: generate arelationship between latency and utilization of a resource in anetworked storage environment using observation based, current andhistorical latency and utilization data, where latency is an indicatorof delay at the resource for processing any request and utilization ofthe resource is an indicator of an extent the resource is being used atany given time; and select an optimal point for the generatedrelationship between latency and utilization, where the optimal point isan indicator of resource utilization beyond which throughput gains for aworkload is smaller than increase in latency.

In yet another aspect, a system having a memory containing machinereadable medium comprising machine executable code having stored thereoninstructions is provided. A processor module coupled to the memoryexecutes the machine executable code to: generate a relationship betweenlatency and utilization of a resource in a networked storage environmentusing observation based, current and historical latency and utilizationdata, where latency is an indicator of delay at the resource forprocessing any request and utilization of the resource is an indicatorof an extent the resource is being used at any given time; and select anoptimal point for the generated relationship between latency andutilization, where the optimal point is an indicator of resourceutilization beyond which throughput gains for a workload is smaller thanincrease in latency.

This brief summary has been provided so that the nature of thisdisclosure may be understood quickly. A more complete understanding ofthe disclosure can be obtained by reference to the following detaileddescription of the various thereof in connection with the attacheddrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The various features of the present disclosure will now be describedwith reference to the drawings of the various aspects. In the drawings,the same components may have the same reference numerals. Theillustrated aspects are intended to illustrate, but not to limit thepresent disclosure. The drawings include the following Figures:

FIG. 1A shows an example of a latency v. utilization curve (LvU), fordetermining headroom, according to one aspect of the present disclosure;

FIG. 1B shows an example of an operating environment for the variousaspects disclosed herein;

FIG. 2A shows an example of a clustered storage system, used accordingto one aspect of the present disclosure;

FIG. 2B shows an example of a performance manager, according to oneaspect of the present disclosure;

FIG. 2C shows an example of handling QOS requests by a storage system,according to one aspect of the present disclosure;

FIG. 2D shows an example of a resource layout used by the performancemanager, according to one aspect of the present disclosure;

FIG. 2E shows an example of managing workloads and resources by theperformance manager, according to one aspect of the present disclosure;

FIG. 3A shows a format for managing various resource objects, accordingto one aspect of the present disclosure;

FIG. 3B shows an example of certain counters that are used, according toone aspect of the present disclosure;

FIG. 4A shows an example of an overall process flow for determining andanalyzing headroom, according to one aspect of the present disclosure;

FIG. 4B shows an example of using a custom operational point on a LvUcurve, according to one aspect of the present disclosure;

FIG. 4C shows an example determining an operational point, according toone aspect of the present disclosure;

FIG. 4D shows a graphical illustration of sampled and actual headroom,according to one aspect of the present disclosure;

FIG. 5 shows a process flow for determining an optimal point using amodel based technique; according to one aspect of the presentdisclosure;

FIG. 6A shows a process flow for determining an optimal point usingobservation based technique, according to one aspect of the presentdisclosure;

FIG. 6B provides details for determining actual headroom using theobservation based technique, according to one aspect of the presentdisclosure;

FIG. 7 shows an example of a storage system, used according to oneaspect of the present disclosure;

FIG. 8 shows an example of a storage operating system, used according toone aspect of the present disclosure; and

FIG. 9 shows an example of a processing system, used according to oneaspect of the present disclosure.

DETAILED DESCRIPTION

As a preliminary note, the terms “component”, “module”, “system,” andthe like as used herein are intended to refer to a computer-relatedentity, either software-executing general purpose processor, hardware,firmware and a combination thereof. For example, a component may be, butis not limited to being, a process running on a hardware processor, ahardware based processor, an object, an executable, a thread ofexecution, a program, and/or a computer.

By way of illustration, both an application running on a server and theserver can be a component. One or more components may reside within aprocess and/or thread of execution, and a component may be localized onone computer and/or distributed between two or more computers. Also,these components can execute from various computer readable media havingvarious data structures stored thereon. The components may communicatevia local and/or remote processes such as in accordance with a signalhaving one or more data packets (e.g., data from one componentinteracting with another component in a local system, distributedsystem, and/or across a network such as the Internet with other systemsvia the signal).

Computer executable components can be stored, for example, atnon-transitory, computer readable media including, but not limited to,an ASIC (application specific integrated circuit), CD (compact disc),DVD (digital video disk), ROM (read only memory), floppy disk, harddisk, EEPROM (electrically erasable programmable read only memory),memory stick or any other storage device, in accordance with the claimedsubject matter.

In one aspect, a performance manager module is provided that interfaceswith a storage operating system to collect quality of service (QOS) data(or performance data) for various resources. QOS provides a certainthroughput (i.e. amount of data that is transferred within a given timeinterval (for example, megabytes per seconds (MBS)), latency and/or anumber of input/output operations that can be processed within a timeinterval, for example, in a second (referred to as IOPS). Latency meansa delay in completing the processing of an I/O request and may bemeasured using different metrics for example, a response time inprocessing I/O requests.

Latency v Utilization Curve:

In one aspect, methods and systems for managing resources in a networkedstorage environment is provided. The resources may be managed based onremaining (or useful) performance capacity at any given time that isavailable for a resource relative to a peak/optimal performance capacitywithout violating any performance expectations. The availableperformance capacity may be referred to as “headroom” that is discussedin detail below. The resource maybe any resource in the networkedstorage environment, including processing nodes and aggregates that aredescribed below in detail. Peak performance capacity of a resource maybe determined according to performance limits that may be set bypolicies (for example, QoS or service level objectives (SLOs) asdescribed below).

In one aspect, the remaining or available performance capacity isdetermined from a relationship between latency and utilization of aresource. FIG. 1A shows an example of one such curve. Latency 133 for agiven resource that is used to process workloads is shown on thevertical, Y-axis, while the utilization 131 of the resource is shown onthe X-axis.

The latency v utilization curve shows an optimal point 137, after whichlatency shows a rapid increase. Optimal point represents maximum (oroptimum) utilization of a resource beyond which an increase in workloadare associated with higher throughput gains than latency increase.Beyond the optimal point, if the workload increases at a resource, thethroughput gains or utilization increase is smaller than the increase inlatency. An optimal point may be determined by a plurality of techniquesdefined below. The optimal point may also be customized based on aservice level that guarantees certain latency/utilization for a user.The use of optimal points are described below in detail.

An operational point 135 shows current utilization of the resource. Theavailable performance capacity is shown as 139. In one aspect, theoperational point 135 may be determined based on current utilization ofa resource. The operational point may also be determined based on theeffect of internal workloads (for example, when a storage volume ismoved), when a storage node is configured as a high availabilityfailover node or when there are workloads that can be throttled ordelayed because they may not be very critical.

In one aspect, headroom (or performance capacity) may be based on thefollowing relationship:

${Headroom} = \frac{{{Optimal}\mspace{14mu} {Point}} - {{Operational}\mspace{14mu} {Point}}}{{Optimal}\mspace{14mu} {Point}}$

Headroom may be based on current utilization and a current optimal pointthat is ascertained based on collected and observed data. This isreferred to “sampled” headroom. The sampled headroom may be modified byupdating the current utilization of the resource to reflect any highavailability node pair load (defined below) or any work that can bethrottled or defined as not being critical to a workload mix. The termworkload mix represents user workloads at a resource. Details forcomputing sampled and actual headroom are provided below.

In aspect, a machine implemented method for a networked storageenvironment is provided. The method includes generating a relationshipbetween latency and utilization of a resource in a networked storageenvironment using observation based, current and historical latency andutilization data, where latency is an indicator of delay at the resourcefor processing any request and utilization of the resource is anindicator of an extent the resource is being used at any given time; andselecting an optimal point for the generated relationship betweenlatency and utilization, where the optimal point is an indicator ofresource utilization beyond which throughput gains for a workload issmaller than increase in latency.

Before describing the processes for generating the latency v utilization(may also be referred to as LvU), the following provides a descriptionof the overall networked storage environment and the resources used inthe operating environment for storing data.

System 100:

FIG. 1B shows an example of a system 100, where the various adaptiveaspects disclosed herein may be implemented. System 100 includes aperformance manager 121 that interfaces with a storage operating system107 of a storage system 108 for receiving QOS data. The performancemanager 121 may be a processor executable module that is executed by oneor more processors out of a memory device.

The performance manager 121 obtains the QOS data and stores it at a datastructure 125. In one aspect, performance manager 121 analyzes the QOSdata for determining headroom for a given resource. Headroom relatedinformation may be stored at data structure 125A that is described belowin detail. Details regarding the various operations performed by theperformance manager 121 for determining headroom are provided below.

In one aspect, storage system 108 has access to a set of mass storagedevices 114A-114N (may be referred to as storage devices 114 or simplyas storage device 114) within at least one storage subsystem 112. Thestorage devices 114 may include writable storage device media such asmagnetic disks, video tape, optical, DVD, magnetic tape, non-volatilememory devices for example, solid state drives (SSDs) includingself-encrypting drives, flash memory devices and any other similar mediaadapted to store information. The storage devices 114 may be organizedas one or more groups of Redundant Array of Independent (or Inexpensive)Disks (RAID). The aspects disclosed are not limited to any particularstorage device type or storage device configuration.

In one aspect, the storage system 108 provides a set of logical storagevolumes (may be interchangeably referred to as volume or storage volume)for providing physical storage space to clients 116A-116N (or virtualmachines (VMs) 105A-105N). A storage volume is a logical storage objectand typically includes a file system in a NAS environment or a logicalunit number (LUN) in a SAN environment. The various aspects describedherein are not limited to any specific format in which physical storageis presented as logical storage (volume, LUNs and others)

Each storage volume may be configured to store data files (or datacontainers or data objects), scripts, word processing documents,executable programs, and any other type of structured or unstructureddata. From the perspective of one of the client systems, each storagevolume can appear to be a single drive. However, each storage volume canrepresent storage space in at one storage device, an aggregate of someor all of the storage space in multiple storage devices, a RAID group,or any other suitable set of storage space.

A storage volume is identified by a unique identifier (Volume-ID) and isallocated certain storage space during a configuration process. When thestorage volume is created, a QOS policy may be associated with thestorage volume such that requests associated with the storage volume canbe managed appropriately. The QOS policy may be a part of a QOS policygroup (referred to as “Policy_Group”) that is used to manage QOS forseveral different storage volumes as a single unit. The QOS policyinformation may be stored at a QOS data structure 111 maintained by aQOS module 109. QOS at the storage system level may be implemented bythe QOS module 109. QOS module 109 maintains various QOS data types thatare monitored and analyzed by the performance manager 121, as describedbelow in detail.

The storage operating system 107 organizes physical storage space atstorage devices 114 as one or more “aggregate”, where each aggregate isa logical grouping of physical storage identified by a unique identifierand a location. The aggregate includes a certain amount of storage spacethat can be expanded. Within each aggregate, one or more storage volumesare created whose size can be varied. A qtree, sub-volume unit may alsobe created within the storage volumes. For QOS management, eachaggregate and the storage devices within the aggregates are consideredas resources that are used by storage volumes.

The storage system 108 may be used to store and manage information atstorage devices 114 based on an I/O request. The request may be based onfile-based access protocols, for example, the Common Internet FileSystem (CIFS) protocol or Network File System (NFS) protocol, over theTransmission Control Protocol/Internet Protocol (TCP/IP). Alternatively,the request may use block-based access protocols, for example, the SmallComputer Systems Interface (SCSI) protocol encapsulated over TCP (iSCSI)and SCSI encapsulated over Fibre Channel (FCP).

In a typical mode of operation, a client (or a VM) transmits one or moreI/O request, such as a CFS or NFS read or write request, over aconnection system 110 to the storage system 108. Storage operatingsystem 107 receives the request, issues one or more I/O commands tostorage devices 114 to read or write the data on behalf of the clientsystem, and issues a CIFS or NFS response containing the requested dataover the network 110 to the respective client system.

System 100 may also include a virtual machine environment where aphysical resource is time-shared among a plurality of independentlyoperating processor executable VMs. Each VM may function as aself-contained platform, running its own operating system (OS) andcomputer executable, application software. The computer executableinstructions running in a VM may be collectively referred to herein as“guest software.” In addition, resources available within the VM may bereferred to herein as “guest resources.”

The guest software expects to operate as if it were running on adedicated computer rather than in a VM. That is, the guest softwareexpects to control various events and have access to hardware resourceson a physical computing system (may also be referred to as a hostplatform or host system) which maybe referred to herein as “hosthardware resources”. The host hardware resource may include one or moreprocessors, resources resident on the processors (e.g., controlregisters, caches and others), memory (instructions residing in memory,e.g., descriptor tables), and other resources (e.g., input/outputdevices, host attached storage, network attached storage or other likestorage) that reside in a physical machine or are coupled to the hostsystem.

In one aspect, system 100 may include a plurality of computing systems102A-102N (may also be referred to individually as host platform/system102 or simply as server 102) communicably coupled to the storage system108 via the connection system 110 such as a local area network (LAN),wide area network (WAN), the Internet or any other interconnect type. Asdescribed herein, the term “communicably coupled” may refer to a directconnection, a network connection, a wireless connection or otherconnections to enable communication between devices.

Host system 102A includes a processor executable virtual machineenvironment having a plurality of VMs 105A-105N that may be presented toclient computing devices/systems 116A-116N. VMs 105A-105N execute aplurality of guest OS 104A-104N (may also be referred to as guest OS104) that share hardware resources 120. As described above, hardwareresources 120 may include processors, memory, I/O devices, storage orany other hardware resource.

In one aspect, host system 102 interfaces with a virtual machine monitor(VMM) 106, for example, a processor executed Hyper-V layer provided byMicrosoft Corporation of Redmond, Wash., a hypervisor layer provided byVMWare Inc., or any other type. VMM 106 presents and manages theplurality of guest OS 104A-104N executed by the host system 102. The VMM106 may include or interface with a virtualization layer (VIL) 123 thatprovides one or more virtualized hardware resource to each OS 104A-104N.

In one aspect, VMM 106 is executed by host system 102A with VMs105A-105N. In another aspect, VMM 106 may be executed by an independentstand-alone computing system, often referred to as a hypervisor serveror VMM server and VMs 105A-105N are presented at one or more computingsystems.

It is noteworthy that different vendors provide different virtualizationenvironments, for example, VMware Corporation, Microsoft Corporation andothers. The generic virtualization environment described above withrespect to FIG. 1B may be customized to implement the aspects of thepresent disclosure. Furthermore, VMM 106 (or VIL 123) may execute othermodules, for example, a storage driver, network interface and others,the details of which are not germane to the aspects described herein andhence have not been described in detail.

System 100 may also include a management console 118 that executes aprocessor executable management application 117 for managing andconfiguring various elements of system 100. Application 117 may be usedto manage and configure VMs and clients as well as configure resourcesthat are used by VMs/clients, according to one aspect. It is noteworthythat although a single management console 118 is shown in FIG. 1B,system 100 may include other management consoles performing certainfunctions, for example, managing storage systems, managing networkconnections and other functions described below.

In one aspect, application 117 may be used to present storage space thatis managed by storage system 108 to clients' 116A-116N (or VMs). Theclients may be grouped into different service levels (also referred toas service level objectives or “SLOs”), where a client with a higherservice level may be provided with more storage space than a client witha lower service level. A client at a higher level may also be providedwith a certain QOS vis-à-vis a client at a lower level.

Although storage system 108 is shown as a stand-alone system, i.e. anon-cluster based system, in another aspect, storage system 108 may havea distributed architecture; for example, a cluster based system of FIG.2A. Before describing the various aspects of the performance manager121, the following provides a description of a cluster based storagesystem.

Clustered Storage System:

FIG. 2A shows a cluster based storage environment 200 having a pluralityof nodes for managing storage devices, according to one aspect. Storageenvironment 200 may include a plurality of client systems 204.1-204.N(similar to clients 116A-116N, FIG. 1B), a clustered storage system 202,performance manager 121, management console 118 and at least a network206 communicably connecting the client systems 204.1-204.N and theclustered storage system 202.

The clustered storage system 202 includes a plurality of nodes208.1-208.3, a cluster switching fabric 210, and a plurality of massstorage devices 212.1-212.3 (may be referred to as 212 and similar tostorage device 114) that are used as resources for processing I/Orequests.

Each of the plurality of nodes 208.1-208.3 is configured to include anetwork module (maybe referred to as N-module), a storage module (maybereferred to as D-module), and a management module (maybe referred to asM-Module), each of which can be implemented as a processor executablemodule. Specifically, node 208.1 includes a network module 214.1, astorage module 216.1, and a management module 218.1, node 208.2 includesa network module 214.2, a storage module 216.2, and a management module218.2, and node 208.3 includes a network module 214.3, a storage module216.3, and a management module 218.3.

The network modules 214.1-214.3 include functionality that enable therespective nodes 208.1-208.3 to connect to one or more of the clientsystems 204.1-204.N over the computer network 206, while the storagemodules 216.1-216.3 connect to one or more of the storage devices212.1-212.3. Accordingly, each of the plurality of nodes 208.1-208.3 inthe clustered storage server arrangement provides the functionality of astorage server.

The management modules 218.1-218.3 provide management functions for theclustered storage system 202. The management modules 218.1-218.3 collectstorage information regarding storage devices 212.

Each node may execute or interface with a QOS module, shown as109.1-109.3 that is similar to the QOS module 109. The QOS module 109may be executed for each node or a single QOS module may be used for theentire cluster. The aspects disclosed herein are not limited to thenumber of instances of QOS module 109 that may be used in a cluster.

A switched virtualization layer including a plurality of virtualinterfaces (VIFs) 201 is provided to interface between the respectivenetwork modules 214.1-214.3 and the client systems 204.1-204.N, allowingstorage 212.1-212.3 associated with the nodes 208.1-208.3 to bepresented to the client systems 204.1-204.N as a single shared storagepool.

The clustered storage system 202 can be organized into any suitablenumber of virtual servers (also referred to as “vservers” or storagevirtual machines (SVM)), in which each SVM represents a single storagesystem namespace with separate network access. Each SVM has a clientdomain and a security domain that are separate from the client andsecurity domains of other SVMs. Moreover, each SVM is associated withone or more VIFs and can span one or more physical nodes, each of whichcan hold one or more VIFs and storage associated with one or more SVMs.Client systems can access the data on a SVM from any node of theclustered system, through the VIFs associated with that SVM. It isnoteworthy that the aspects described herein are not limited to the useof SVMs.

Each of the nodes 208.1-208.3 is defined as a computing system toprovide application services to one or more of the client systems204.1-204.N. The nodes 208.1-208.3 are interconnected by the switchingfabric 210, which, for example, may be embodied as a Gigabit Ethernetswitch or any other type of switching/connecting device.

Although FIG. 2A depicts an equal number (i.e., 3) of the networkmodules 214.1-214.3, the storage modules 216.1-216.3, and the managementmodules 218.1-218.3, any other suitable number of network modules,storage modules, and management modules may be provided. There may alsobe different numbers of network modules, storage modules, and/ormanagement modules within the clustered storage system 202. For example,in alternative aspects, the clustered storage system 202 may include aplurality of network modules and a plurality of storage modulesinterconnected in a configuration that does not reflect a one-to-onecorrespondence between the network modules and storage modules.

Each client system 204.1-204.N may request the services of one of therespective nodes 208.1, 208.2, 208.3, and that node may return theresults of the services requested by the client system by exchangingpackets over the computer network 206, which may be wire-based, opticalfiber, wireless, or any other suitable combination thereof.

Performance manager 121 interfaces with the various nodes and obtainsQOS data for QOS data structure 125. Details regarding the variousmodules of performance manager are now described with respect to FIG.2B.

Performance Manager 121:

FIG. 2B shows a block diagram of system 200A with details regardingperformance manager 121 and a collection module 211, according to oneaspect. Performance manager 121 uses the concept of workloads fortracking QOS data for managing resource usage in a networked storageenvironment. At a high level, workloads are defined based on incomingI/O requests and use resources within storage system 202 for processingI/O requests. A workload may include a plurality of streams, where eachstream includes one or more requests. A stream may include requests fromone or more clients. An example, of the workload model used byperformance manager 121 is shown in FIG. 2F and described below indetail.

Performance manager 121 collects a certain amount of data (for example,data for 3 hours or 30 data samples) of workload activity. Aftercollecting the QOS data, performance manager 121 determines the headroomfor a resource, as described below in detail. Performance manager 121uses the headroom to represent available resource capacity at any giventime.

Performance 121 includes a current headroom coordinator 221 thatincludes a plurality of sub-modules including a filtering module 237, anoptimal point module 225 and an analysis module 223. The filteringmodule 237 filters collected QOS data (shown as incoming data 229) andprovides the filtered data to the optimal point module 225. The optimalpoint module 225 then determines an optimal point 137 for a LvU curve.In one aspect, the optimal point module 225 determines the optimal pointusing a plurality of techniques and the technique that provides the mostreliable value (i.e. with the highest confidence level) is selected.

The optimal point with the LvU curve is provided to the analysis module223 that uses the curve and determines the headroom based on one or moreoperational points 135. The headroom information may be stored in aheadroom data structure 125A. Details of using the filtering module 237,optimal point module 225 and the analysis module 223 are provided below.

In one aspect, the current headroom coordinator 221 and its componentsmay be implemented as a processor executable, application programminginterface (API) which provides a set of routines, protocols, and toolsfor building a processor executable software application that can beexecuted by a computing device. When the current headroom coordinator221 is implemented as API, then it provides software components in termsof its operations, inputs, outputs, and underlying types. The API may beimplemented as a plug-in API which integrates headroom computation andanalysis with other management applications.

When the current headroom coordinator 221 is implemented as an API, thenvarious inputs may be provided for determining headroom. For example,inputs may include a resource identifier that identifies a resourcewhose performance capacity is to be computed. The outputs may includeheadroom values, a confidence factor, and a time range for which theheadroom is computed and other information

Referring now to FIG. 2B, System 200A shows two clusters 202A and 202B,both similar to cluster 202 described above. Each cluster includes theQOS module 109 for implementing QOS policies and appropriate countersfor collecting information regarding various resources. Cluster 1 202Amay be accessible to clients 204.1 and 204.2, while cluster 2 202B isaccessible to clients 204.3/204.4. Both clusters have access to storagesubsystems 207 and storage devices 212.1/212.N.

Clusters 202A and 202B communicate with collection module 211. Thecollection module 211 may be a standalone computing device or integratedwith performance manager 121. The aspects described herein are notlimited to any particular configuration of collection module 211 andperformance manager 121.

Collection module 211 includes one or more acquisition modules 219 forcollecting QOS data from the clusters. The data is pre-processed by thepre-processing module 215 and stored as pre-processed QOS data 217 at astorage device (not shown). Pre-processing module 215 formats thecollected QOS data for the performance manager 121. Pre-processed QOSdata 217 is provided to a collection module interface 231 of theperformance manager 121 via the performance manager interface 213. QOSdata received from collection module 211 is stored as QOS data structure125 (shown as incoming data 229) and used by the filtering module 237,before the data is used for computing the optimal point 137.

In one aspect, the performance manager 121 includes a GUI 229. Client205 may access headroom analysis results using GUI 229. Beforedescribing the various processes involving performance manager 121 andits components, the following provides an overview of QOS in general, asused by the various aspects of the present disclosure.

QOS Overview:

As shown in FIG. 2C, the network module 214 of a cluster includes anetwork interface 214A for receiving requests from clients. Networkmodule 214 executes a NFS module 214C for handling NFS requests, a CIFSmodule 214D for handling CIFS requests, a SCSI module 214E for handlingiSCSI requests and an others module 214F for handling “other” requests.A node interface 214G is used to communicate with QOS module 109,storage module 216 and/or another network module 214. QOS managementinterface 214B is used to provide QOS data from the cluster tocollection module 211 for pre-processing data.

QOS module 109 includes a QOS controller 109A, a QOS request classifier109B and QOS policy data structure (or Policy_Group) 111. The QOS policydata structure 111 stores policy level details for implementing QOS forclients and storage volumes. The policy determines what latency andthroughput rate is permitted for a client as well as for specificstorage volumes. The policy determines how I/O requests are processedfor different volumes and clients.

The storage module 216 executes a file system 216A (a part of storageoperating system 107 described below) and includes a storage layer 216Bto interface with storage device 212.

NVRAM 216C of the storage module 216 may be used as a cache forresponding to I/O requests. In one aspect, for executing a writerequest, the write data associated with the write request is firststored at a memory buffer of the storage module 216. The storage module216 acknowledges that the write request is completed after it is storedat the memory buffer. The data is then moved from the memory buffer tothe NVRAM 216C and then flushed to the storage device 212, referred toas consistency point (CP).

An I/O request arrives at network module 214 from a client or from aninternal process directly to file system 216A. Internal process in thiscontext may include a de-duplication module, a replication engine moduleor any other entity that needs to perform a read and/or write operationat the storage device 212. The request is sent to the QOS requestclassifier 109B to associate the request with a particular workload. Theclassifier 109B evaluates a request's attributes and looks for matcheswithin QOS policy data structure 111. The request is assigned to aparticular workload, when there is a match. If there is no match, then adefault workload may be assigned.

Once the request is classified for a workload, then the requestprocessing can be controlled. QOS controller 109A determines if a ratelimit (i.e. a throughput rate) for the request has been reached. If yes,then the request is queued for later processing. If not, then therequest is sent to file system 216A for further processing with acompletion deadline. The completion deadline is tagged with a messagefor the request.

File system 216A determines how queued requests should be processedbased on completion deadlines. The last stage of QOS control forprocessing the request occurs at the physical storage device level. Thiscould be based on latency with respect to storage device 212 or overallnode capacity/utilization as described below in detail.

Performance Model:

FIG. 2D shows an example of a queuing structure used by the performancemanager 121 for determining headroom, according to one aspect. A userworkload enters the queuing network from one end (i.e. at 233) andleaves at the other end.

Various resources are used to process I/O requests. As an example, thereare may be two types of resources, a service center and a delay centerresource. The service center is a resource category that can berepresented by a queue with a wait time and a service time (for example,a processor that processes a request out of a queue). The delay centermay be a logical representation for a control point where a requeststalls waiting for a certain event to occur and hence the delay centerrepresents the delay in request processing. The delay center may berepresented by a queue that does not include service time and insteadonly represents wait time. The distinction between the two resourcetypes is that for a service center, the QOS data includes a number ofvisits, wait time per visit and service time per visit for incidentdetection and analysis. For the delay center, only the number of visitsand the wait time per visit at the delay center are used, as describedbelow in detail.

Performance manager 121 uses different flow types for its analysis. Aflow type is a logical view for modeling request processing from aparticular viewpoint. The flow types include two categories, latency andutilization. A latency flow type is used for analyzing how longoperations take at the service and delay centers. The latency flow typeis used to identify a workload whose latency has increased beyond acertain level. A typical latency flow may involve writing data to astorage device based on a client request and there is latency involvedin writing the data at the storage device. The utilization flow type isused to understand resource consumption of workloads and may be used toidentify resource contention.

Referring now to FIG. 2D, delay center network 235 is a resource queuethat is used to track wait time due to external networks. Storageoperating system 107 often makes calls to external entities to wait onsomething before a request can proceed. Delay center 235 tracks thiswait time using a counter (not shown).

Network module delay center 237 is another resource queue where I/Orequests wait for protocol processing by a network module processor.This delay center 237 is used to track the utilization/capacity of thenetwork module 216. Overutilization of this resource may cause latency,as described below in detail.

NV_RAM transfer delay center 273 is used to track how the non-volatilememory may be used by cluster nodes to store write data before, the datais written to storage devices 212, in one aspect, as described below indetail.

A storage aggregate (or aggregate) 239 is a resource that may includemore than one storage device for reading and writing information.Aggregate 239 is tracked to determine if the aggregate is fragmentedand/or over utilized, as described below in detail.

Storage device delay center 241 may be used to track the utilization ofstorage devices 212. In one aspect, storage device utilization is basedon how busy a storage device may be in responding to I/O requests.

In one aspect, storage module delay center 245 is used for tracking nodeutilization. Delay center 245 is tracked to monitor the idle time for aCPU used by the storage module 216, the ratio of sequential and paralleloperations executed by the CPU and a ratio of write duration andflushing duration for using NVRAM 216C or an NVRAM at the storage module(not shown).

Nodes within a cluster communicate with each other. These may causedelays in processing I/O requests. The cluster interconnect delay center247 is used to track the wait time for transfers using the clusterinterconnect system. As an example, a single queue maybe used to trackdelays due to cluster interconnects.

There may also be delay centers due to certain internal processes ofstorage operating system 107 and various queues may be used to trackthose delays. For example, a queue may be used to track the wait for I/Orequests that may be blocked for file system reasons. Another queue(Delay_Center_Susp_CP) may be used to represent the wait time forConsistency Point (CP) related to the file system 216A. During a CP,write requests are written in bulk at storage devices and this willtypically cause other write requests to be blocked so that certainbuffers are cleared.

Workload Model:

FIG. 2E shows an example, of the workload model used by performancemanager 121, according to one aspect. As an example, a workload mayinclude a plurality of streams 251A-251N. Each stream may have aplurality of requests 253A-253N. The requests may be generated by anyentity, for example, an external entity 255, like a client system and/oran internal entity 257, for example, a replication engine thatreplicates storage volumes at one or more storage location.

A request may have a plurality of attributes, for example, a source, apath, a destination and I/O properties. The source identifies the sourcefrom where a request originates, for example, an internal process, ahost or client address, a user application and others.

The path defines the entry path into the storage system. For example, apath may be a logical interface (LIF) or a protocol, such as NFS, CIFS,iSCSI and Fibre Channel protocol. A destination is the target of arequest, for example, storage volumes, LUNs, data containers and others.I/O properties include operation type (i.e. read/write/other), requestsize and any other property.

In one aspect, streams may be grouped together based on client needs.For example, if a group of clients make up a department on two differentsubnets, then two different streams with the “source” restrictions canbe defined and grouped within the same workload. Furthermore, requeststhat fall into a workload are tracked together by performance 121 forefficiency. Any requests that don't match a user or system definedworkload may be assigned to a default workload.

In one aspect, workload streams may be defined based on the I/Oattributes. The attributes may be defined by clients. Based on thestream definition, performance manager 121 tracks workloads, asdescribed below.

Referring back to FIG. 2E, a workload uses one or more resources forprocessing I/O requests shown as 271A-271N as part of a resource object259. The resources include service centers and delay centers that havebeen described above with respect to FIG. 2D. For each resource, acounter/queue is maintained for tracking different statistics (or QOSdata) 261. For example, a response time 263, and a number of visits 265,a service time (for service centers) 267, a wait time 269 andinter-arrival time 275 are tracked. Inter-arrival time 275 is used totrack when an I/O request for reading or writing data is received at aresource. The term QOS data as used throughout this specificationincludes one or more of 263, 265, 267 and 269 according to one aspect.

Performance manager 121 may use a plurality of counter objects forresource monitoring and headroom analysis, according to one aspect.Without limiting the various adaptive aspects, an example of the variouscounter objects are shown and described in Table I below:

TABLE I Workload Object Counters Description OPS A number of workloadoperations that are completed during a measurement interval, forexample, a second. Read_ops A number of workload read operations thatare completed during the measurement interval. Write_ops A number ofworkload write operations that are completed during the measurementinterval. Total_data Total data read and written per second by aworkload. Read_data The data read per second by a workload. Write_dataThe data written per second by a workload. Latency The average responsetime for I/O requests that were initiated by a workload. Read_latencyThe average response time for read requests that were initiated by aworkload. Write_latency The average response time for write requeststhat were initiated by a workload. Classified Requests that wereclassified as part of a workload. Read_IO_type The percentage of readsserved from various components (for example, buffer cache, ext_cache ordisk). Concurrency Average number of concurrent requests for a workload.Interarrival_time_sum_squares Sum of the squares of the Inter-arrivaltime for requests of a workload.

Without limiting the various aspects of the present disclosure, Table IIbelow provides an example of the details associated with the objectcounters that are monitored by the performance manager 121, according toone aspect:

TABLE II Workload Detail Object Counter Description Visits A number ofvisits to a physical resource per second; this value is grouped by aservice center. Service_Time A workload's average service time per visitto the service center. Wait_Time A workload's average wait time pervisit to the service center.

Object Hierarchy:

FIG. 3A shows an example of a format 300 for tracking informationregarding different resources that are used within a clustered storagesystem (for example, 202, FIG. 2A). Each resource is identified by aunique resource identifier value that is maintained by the performancemanager 121. The resource identifier value may be used to obtainavailable performance capacity (headroom) of a resource.

Format 300 maybe hierarchical in nature where various objects may haveparent-child, peer and remote peer relationships, as described below. Asan example, format 300 shows a cluster object 302 that may becategorized as a root object type for tracking cluster level resources.The cluster object 302 is associated with various child objects, forexample, a node object 306, QOS network object 304, a portset object318, a SVM object 324 and a policy group 326. The cluster object 302stores information regarding the cluster, for example, the number ofnodes it may have, information identifying the nodes; and any otherinformation.

The QOS network object 304 is used to monitor network resources, forexample, network switches and associated bandwidth used by a clusteredstorage system.

The cluster node object 306 stores information regarding a node, forexample, a node identifier and other information. Each cluster nodeobject 306 is associated with a pluralities of child objects, forexample, a cache object 308, a QOS object for a storage module 310, aQOS object for a network module 314, a CPU object 312 and an aggregateobject 316. The cache object 308 is used to track utilization/latency ofa cache (for example, NVRAM 216C, FIG. 2D). The QOS storage module 310tracks the QOS of a storage module defined by a QOS policy datastructure 111 described above in detail with respect to FIG. 2D. The QOSnetwork module object 314 tracks the QOS for a network module. The CPUobject 312 is used to track CPU performance and utilization of a node.

The aggregate object 316 tracks the utilization/latency of a storageaggregate that is managed by a cluster node. The aggregate object mayhave various child objects, for example, a flash pool object 332 thattracks usage of a plurality of flash based storage devices (shown as“flash pool”). The flash pool object 332 may have a SSD disk object 336that tracks the actual usage of specific SSD based storage devices. TheRAID group 334 is used to track the usage of storage devices configuredas RAID devices. The RAID object 334 includes a storage device object338 (shown as a HDD (hard disk drive) that tracks the actual utilizationof the storage devices.

Each cluster is provided a portset having a plurality of ports that maybe used to access cluster resources. A port includes logic and circuitryfor processing information that is used for communication betweendifferent resources of the storage system. The portset object 318 tracksthe various members of the portset using a port object 320 and a LIFobject 322. The LIF object 322 includes a logical interface, forexample, an IP address, while the port object 320 includes a portidentifier for a port, for example, a world-wide port number (WWPN). Itis noteworthy that the port object 320 is also a child object of node306 that may use a port for network communication with clients.

A cluster may present one or more SVMs to client systems. The SVMs aretracked by the SVM object 324, which is a child object of cluster 302.Each cluster is also associated with a policy group that is tracked by apolicy group object 326. The policy group 326 is associated with SVMobject 324 as well as storage volumes and LUNs. The storage volume istracked by a volume object 328 and the LUN is tracked by a LUN object330. The volume object 328 includes an identifier identifying a volume,size of the volume, clients associated with the volume, volume type(i.e. flexible or fixed size) and other information. The LUN object 330includes information that identifies the LUN (LUNID), size of the LUN,LUN type (read, write or read and write) and other information.

FIG. 3B shows an example of some additional counters that are used forheadroom analysis, described below in detail. These counters are relatedto nodes and aggregates and are in addition to the counters of Table Idescribed above. For example, counter 306A is used to track theutilization i.e. idle time for each node processor. Node latency counter306B tracks the latency at the nodes based on operation types, i.e. readand write operations. The latency may be based on the total number ofvisits at a storage system node/number of operations per second for aworkload. This value may not include internal or system defaultworkloads, as described below in detail.

Aggregate utilization is tracked using counter 316A that tracks theduration of how busy a device may be for processing user requests. Anaggregate latency counter 316B tracks the latency due to the storagedevices within an aggregate. The latency may be based on a measureddelay for each storage device in an aggregate. The use of these countersfor headroom analysis is described below in detail.

Headroom Computation and Analysis:

FIG. 4A shows an overall machine implemented process flow 400 fordetermining and analyzing headroom, according to one aspect of thepresent disclosure. The various process blocks may be executed byperformance manager 121. It is noteworthy that the process blocks may beexecuted by processor executable application programming interface(APIs) that may be made available at a management console or anycomputing device.

The process begins in block B402, when the storage system 108 isoperational and data has been stored at the storage devices. In blockB404, performance data (for example, latency and utilization data,inter-arrival times and/or service times) for at least the cluster nodesand aggregates has been collected. The collected data is provided to theperformance manager 121. In one aspect, current and historical QOS datamay both be accessed by the performance manager 121 for headroomanalysis. The performance manager 121 also obtains information regardingany events that may have occurred at the storage system level associatedwith the QOS data. Any policy information that is associated with theresource for which the QOS data is also obtained by the performancemanager 121.

In block B406, the filtering module 237 filters the collected data. Inone aspect, potential erroneous observations such as unreasonable largelatency values, variances, service times or utilizations are identified.If there is any data associated with unusual events like hardwarefailure or network failure that may affect performance may be discarded.For example, if a flash memory card used by a node fails and has to bereplaced, then the latency for processing I/O requests with the failedcard may be unreasonably high and hence data associated with that nodemay not be reliable for headroom computations. Any outliers in thecollected and historical QOS data may also be removed (for example, thetop 5-10% and the bottom 5-10% of the latency and utilization values maybe discarded).

In one aspect, filtering module 237 may also insert missing data,according to one aspect. For example, service times for differentresources are expected to be within a range based on collectedhistorical service time data. If the collected data have a highcoefficient of variation, then the collected data may not be reliableand hence may have to be corrected.

After the data is filtered, in block B408, one or more LvU curves aregenerated and an optimal point is determined by the optimal point module225. In one aspect, as an example, different techniques (for example,model based, observation based or any other techniques) are used togenerate the LvU curves and compute the optimal point. The techniquethat provides the most reliable optimal point is used for headroomanalysis.

The model based technique uses current observations and queueing modelsto generate the LvU curve. The model based technique uses inter-arrivaltimes and service times for a resource. The inter-arrival times trackthe arrival times for I/O requests at a resource, while the servicetimes track the duration for servicing user based I/O requests. Theobservation based technique uses both current and historicalobservations of latency and utilizations for generating LvU curves.Details regarding the various optimal point techniques are providedbelow with respect to FIGS. 5 and 6. It is noteworthy that the variousadaptive aspects of the present disclosure are not limited to anyspecific technique.

In block B410, the optimal point with the highest confidence level (i.e.the most reliable optimal point value) is selected and provided to theanalysis module in block B412. In another aspect, the optimal point maybe based on a policy based input. FIG. 4B shows an example of a LvUcurve 428, which uses a SLO input (for example, from a policy) 430. TheSLO input defines a latency limit that is assigned for user/resource.The custom optimal point is determined by the intersection of the SLOinput and the LvU curve, shown as 432.

FIG. 4C shows an example 434 for identifying the optimal point using the“point of diminishing returns” approach. In FIG. 4C the intersection ofa half latency v utilization curve 436 and the overall latency curve 438may be identified as the optimal point.

In block B414, the analysis module 223 determines the headroom (forexample, 139, FIG. 1A) using the optimal point and an operational point.In one aspect, different operational points may be used for a resourcebased on the operating environment and how the resources are being used.For example, a current total utilization may be used as an operationalpoint with the presumption that the current total utilization may beused to process a workload mix. As described above, a workload mixrepresents all user workloads utilizing one or more resources. Thisprovides a sampled headroom for a resource.

In another aspect, a custom operational point may be used when a volumeis identified in a policy. In another aspect, the analysis module 223may ascertain the effect of moving workloads which may affectutilization and the operational point. In yet another aspect, theutilization of a node pair that are configured as high availability (HA)pair nodes is considered for the operational point. When nodes operateas HA pair nodes and if one of the nodes becomes unavailable, then theother node takes over workload processing. In this instance,latency/utilization of both the nodes is used for determining theoperational point and computing the headroom. This headroom analysis isreferred to as the actual headroom.

FIG. 4D shows a graphical illustration of headroom variation for aresource, based on the analysis performed by the analysis module 223.The Y-axis shows the utilization 431 of the resource and X-axis showsduration 433. FIG. 4D shows an example of current sampled headroom as435A and minimum sampled headroom as 435B. The sampled headroom issimply based on current observation values. These may be based on themodel based technique, observation based or any other technique.

The actual headroom is shown by the curve 437. The minimum actualheadroom is shown 445. The minimum headroom is determined by evaluatinginternal workloads 439, HA node workload 443 and critical workload 441.In one aspect, as described above, the operational points for internalworkloads, HA node workloads and critical workloads are determined andthen used to determine the actual headroom.

Referring back to FIG. 4A, in block B416, the plurality of headroomvalues are stored at data structure 125A and may also be presented tothe user. Headroom information stored at headroom data structure 125Amay include the following fields that are described in Table III below:

TABLE III Columns Description Resource metadata This field identifiesthe resource whose sampled headroom is being stored at data structure125A Index counter data This field provides key identifiers for aworkload mix e.g. service time, utilization, latency Time countersSample time Observation-based Optimal Point - Based on Utilization,IOPS, estimates Latency Optimal Point - Confidence intervals CustomOptimal Point - Based on Utilization, IOPS, latency Custom Optimal PointPolicy Custom Optimal Point - Confidence intervals Curve parametersValidity Indicator if picked for sampled headroom calculationsModel-based estimates Optimal Point - Based on Utilization, IOPS,Latency Optimal Point - Confidence intervals Custom Optimal Point -Utilization, IOPS, Latency Custom Optimal Point Policy Optimal Point -Confidence intervals Optimal Point - Confidence intervals ValidityIndicator if picked for sampled headroom calculations Operational PointsOperational Point - Entire workload mix Operational Point - HA Nodepairs Operational Point - Internal throttled workloads OperationalPoint - Custom based on SLO Headroom Values Sampled headroom Actualheadroom - HA Node pairs Actual headroom - (HA + Internal throttledworkloads) Actual headroom - Custom

In one aspect, headroom data structure 125A may be used for futureanalysis and historical comparison. Data structure 125A may also be madeavailable to APIs that are used by third-party or client systems thatare monitoring a resource using a management application (for example,117, FIG. 1A).

The process of FIG. 4A provides a method for filtering performance dataassociated with a resource used in a networked storage environment forreading and writing data at a storage device; and then determiningavailable performance capacity of the resource using the filteredperformance data. The available performance capacity is based on optimumutilization of the resource and actual utilization of the resource,where utilization of the resource is an indicator of an extent theresource is being used at any given time, the optimum utilization is anindicator of resource utilization beyond which throughput gains for aworkload is smaller than increase in latency and latency is an indicatorof delay at the resource in processing the workload.

Model Based Optimal Point Determination:

FIG. 5 shows a process 500 for generating LvU curves and determining anoptimal point using a model-based technique for block B408 of FIG. 4A,according to one aspect. The model based technique begins in block B502.The model based technique uses analytic models with inter-arrival timesat the resources (average and variance) as well as the service times(average and variance) for each resource to process a workload mix. Aqueueing model is used for evaluating each resource in the networkedstorage environment. As an example, each resource may be modelled as aGI/G/I queue such that that a service center where requests arriveaccording to a general independent stochastic process and are servedaccording to a general stochastic process by a single node, according toFCFC (First Come, First Served) methodology. If the service centerincludes N node resources, then the queueing model is GI/G/n. In oneaspect, I/O request arrivals and service at a resources areparameterized at any given time. In one aspect, as described below,different queuing models are used for different resources.

In block B504, the optimal point module uses the GI/G/N (where N is thenumber of cores that act as servers in the queuing model) model for noderesources, for example, a multi-core CPU of a node.

In block B506, SSD and hard drive aggregates are queued under GI/G/1model by the optimal point module 225 because in an aggregate, the I/Orequests are expected to be uniformly served by all the storage devicesin the aggregate and hence GI/G/1 is an accurate representation.

When an aggregate is a hybrid aggregate i.e. includes both SSD and harddrives, then a GI/G/1 queueing model under the shortest job first (SJF)scheduling policy is used in block B508 by the optimal point module 225.The reason for using this model is because in hybrid aggregates, theservice time may be variable since some I/O requests are served at afaster rate while others at a lower rate.

In block B510, the estimated latency is determined by the optimal pointmodule 225 for each resource using the inter-arrival time and servicetime. The latency may be expressed as T_(r). In one aspect, Kingman'sformula for GI/G/1 queues maybe used to estimate T_(r) of the resourcebased on the equation provided below:

$\begin{matrix}{T_{r} = {T_{s} + {\frac{\rho \; {T_{s}\left( {c_{a}^{2} + c_{s}^{2}} \right)}}{2\left( {1 - \rho} \right)}.}}} & (1)\end{matrix}$

-   -   T_(r) is the expected service time at the resource    -   ρ is the current utilization in the resource    -   c_(a) ² and c_(s) ² are the squared coefficient of        variations (CV) for inter-arrival times and service times at the        resource, respectively.        Kingman's formula is an approximation for the GI/G/1 queues. And        hence a correction factor G_(KLB) may be used for correcting the        expected latency defined by equation [2] below:

$\begin{matrix}{G_{KLB} = \left\{ \begin{matrix}{{\exp\left( {\frac{2}{3} \cdot \frac{1 - \rho}{P_{n}} \cdot \frac{\left( {1 - c_{a}^{2}} \right)^{2}}{c_{a}^{2} + c_{s}^{2}}} \right)},} & {0 \leq c_{a}^{2} \leq 1} \\{{\exp \left( {{- \left( {1 - \rho} \right)}\frac{c_{a}^{2} - 1}{c_{a}^{2} + c_{s}^{2}}} \right)},} & {c_{a}^{2} > 1}\end{matrix} \right.} & (2)\end{matrix}$

The incorporation of G_(KLB) modifies the Kingman formula as:

$\begin{matrix}{T_{r} = {T_{s} + {\frac{\rho \; {T_{s}\left( {c_{a}^{2} + c_{s}^{2}} \right)}}{2\left( {1 - \rho} \right)}{G_{KLB}.}}}} & (3)\end{matrix}$

which tend to return lower latency values compared to (1).

Latency in a GI/G/1/SJF (Shortest Job First) queue the latency isdetermined by:

${T_{r}(x)} = {T_{s} + {\frac{{\rho (x)}{T_{s}\left( {c_{a}^{2} + c_{s}^{2}} \right)}}{2\left( {1 - {\rho (x)}} \right)}G_{KLB}}}$

Where x is a specific service time in the full range [s_(min), s_(max)]of the service times at the resource and

ρ(x)=ρT _(s)∫_(s) _(min) ^(x) tf(t)dt

In the case of multiple servers the latency formula (3) described aboveis modified as

${T_{r} = {T_{s} + {\frac{P_{n}\; {T_{s}\left( {c_{a}^{2} + c_{s}^{2}} \right)}}{2{n\left( {1 - \rho} \right)}}G_{KLB}}}},$

Where n is the number of servers and

$P_{n} = {\frac{\left( {n\; \rho} \right)^{n}}{{n!}\left( {1 - \rho} \right)} \cdot {\left\lbrack {{\sum\limits_{k = 0}^{n - 1}\frac{\left( {m\; \rho} \right)^{k}}{k!}} + \frac{\left( {n\; \rho} \right)^{n}}{{n!}\left( {1 - \rho} \right)}} \right\rbrack^{- 1}.}}$

It is noteworthy that P_(n) is an estimation of the effectiveutilization in the system with n servers. The queuing system is notconsidered busy until all servers are busy. This is captured byexpressing business as a function of the number of servers beingutilized (one component in P_(n) for each possible busy servers).

At each node resource there may be three traffic types: high priority,low priority and CP operations. The accuracy of the models depends howthese three traffic types are interleaved to generate the final queuingmodel. If we assume the traffic at the storage module is managedaccording to priority levels of these types, the latency of highpriority traffic, may be determined by [1]:

$T_{r,1} = {T_{s,1} + {\frac{P_{n}}{2{n\left( {1 - \rho} \right)}\rho}{\sum\limits_{i = 1}^{N}{\rho_{i}{T_{s,i}\left( {c_{a,i}^{2} + c_{s,i}^{2}} \right)}{G_{KLB}.}}}}}$

Here the summation is over % levels of priorities and the subscript irefers to parameters of that type of traffic. Note that i=1 refers tohigh priority traffic.

In another aspect, all types of traffic maybe combined into a singlestream without any batching or priority assumptions. In that case theresulting variance when three types of traffic is mixed is given by:

${\sigma_{mix}^{2} = {{\frac{n_{1}}{n_{1} + n_{2} + n_{3}}\left( {\sigma_{1}^{2} + \left( {\mu_{mix} - \mu_{1}} \right)^{2}} \right)} + {\frac{n_{2}}{n_{1} + n_{2} + n_{3}}\left( {\sigma_{2}^{2} + \left( {\mu_{mix} - \mu_{2}} \right)^{2}} \right)\frac{n_{3}}{n_{1} + n_{2} + n_{3}}\left( {\sigma_{3}^{2} + \left( {\mu_{mix} - \mu_{3}} \right)^{2}} \right)}}},$

where n_(i) and μ_(i) are the sample size and the mean of each traffictype and

$\mu_{mix} = {{\frac{n_{1}}{n_{1} + n_{2} + n_{3}}\mu_{1}} + {\frac{n_{2}}{n_{1} + n_{2} + n_{3}}\mu_{2}} + {\frac{n_{3}}{n_{1} + n_{2} + n_{3}}{\mu_{3}.}}}$

Once the variance and the mean of the inter-arrival times (or servicetimes) the coefficient of variation associated with each process iscomputed and used within the GI/G/1 GI/G/n queuing formulas describedabove. If the variances are large and undesirable, the sample sizes n₂and n₃, which correspond to low priority and CP traffic may be reducedby:

$n_{i} = {n_{i}\frac{n_{i}}{n_{1} + n_{2} + n_{3}}}$

Once the latency is determined by the optimal point module 225 using theforegoing models, in block B512, the analysis model 223 generates theLvU curves and determines the optimal point and the confidence factorassociated with the optimal point. In one aspect, the confidence factormay be 10-150 of the determined optimal point.

The optimal point may also be determined based on policy settings suchas SLO limits (FIG. 4B) or by identifying the point of diminishingreturns in the LvU curve (FIG. 4C) such that increase in utilization issmaller than increase in latency.

In one aspect, the model based technique described above with respect toFIG. 5 has various advantages. The model based technique avoids the needfor computational intensive curve extrapolation techniques, or complexmethodologies. The model based technique provides a fast and efficientway to estimate headroom.

Observation Based Optimal Point Determination:

FIG. 6A shows a process 600 for generating LvU curves using theobservation based technique, according to one aspect of the presentdisclosure. Process 600 may also be part of block B408 of FIG. 4Adescribed above. In one aspect, observation based LvU curves are basedon measured observation of latency and utilization of a resource. Theprocess recognizes that since storage operating system 107 operationscan be complex, it is desirable to observe, record and categorize therelationship between latency and utilization.

Because performance capabilities of a resource are identified viaobservations, it is desirable to identify the proper resources andprocesses. In block B604, the proper resource is identified using aresource identifier. For example, a storage and network node may beidentified as the resource for monitoring. Various counters may be usedto track the performance of each node, as described above with respectto FIG. 3B. Aggregates with storage devices may also be identified formonitoring. In one aspect, nodes and aggregates are used by both userand storage system tasks to service I/O requests.

In B606, latency and utilization data is collected by the performancemanager 121 for the resources identified in block B604. In one aspect,latency and utilization data is collected for each monitored node andaggregate. As described above, counter data for counters 306A, 306B,316A and 316B are collected. Counter 306A data is tracked by each node.In one aspect, counter 306A may track the time a processor node is idle,which indicates how busy the processor may have been over a givenduration. Latency counter 306B collects latency data for both thestorage and network modules. In one aspect, the latency may be based ona total number of visits at each node/number of operations per secondprocessed by each node. This value may not include internal or systemdefault workloads.

Aggregate utilization is tracked using counter 316A that tracks theduration of how busy a device may be for processing user requests. Theaggregate latency counter 316B tracks the latency of the storage deviceswithin an aggregate. The latency tracks the delay at each storagedevice. In one aspect, latency at hard drives is higher that the latencyat solid state storage devices.

In block B608, the collected data for the workload is pre-processed andfiltered by the optimal point module 225 using a workload mix signature.The received data is pre-processed for enhancing the accuracy andsmoothness of the LvU curve.

A LvU curve captures the trend of how a resource sustains the demand ofa workload mix. If the workload mix changes over time, then theresulting curve may be distorted. In one aspect, service time of acurrent workload may be used to search for stored historical latency andutilization data. The historical data for the same service time is usedto augment collected data in block B608. It is noteworthy that otherparameters, for example, read/write ratio and others may be used tofilter the data.

In yet another aspect, collected data may be filtered based on timeusing the assumption that in the short-term the workload mix will staythe same. This means that the observations in the immediate past aremore likely to have a similar workload mix and can be used to generate acurve. In one aspect, for different measured latencies, the optimalpoint module 225 estimates a (utilization, latency) value by removingobservations that may be at a higher and lower end. For differentlatencies measured for the same utilization, mean estimators may be usedto reduce the impact of outliers.

In block B610, when there are missing values in a range of collecteddata, then the missing values are interpolated between two observedutilizations by the optimal point module 225. One way to interpolate thedata is by using historical data for similar workload mix.

In block B612, the optimal point module 225 extrapolates incomplete LvUcurve. The curve is extrapolated when after removing outlier values andusing historical data to interpolate missing values, the process stillgenerates an incomplete curve. In such an instance, the incomplete curvemay be extrapolated. Different techniques may be used to extrapolate thelatency v. utilization curves. For example, linear extrapolation,Newton-Gauss geometric parametric fit and other techniques.

In block B614, the optimal points as described above with respect toFIG. 4A are determined by the optimal point module 225. A confidencefactor for each calculated optimal point is computed. The confidencefactor may be based on the quality of the curve generated fromobservations from a single workload mix; range of observed utilizationsin the available data and the distance between the largest utilizationvalue and the optimal point utilization value. The confidence factor maybe computed by determining a mean distance of the observations from thefitted curve; the range of utilizations, such that the smaller the rangehigher the confidence factor or bound; and farther the optimal pointfrom the maximum utilization, the wider the confidence bound. Theconfidence bounds are a prediction strength that quantifies theconfidence in the estimated value. Prediction strength is an inverse ofthe width of the confidence bounds.

FIG. 6B shows an example of a process 616 for using the observationtechnique results for generating the actual headroom, according to oneaspect of the present disclosure.

The process begins in block B620. In block B622, the analysis module 223identifies the workload or workload set that need to be considered foran internal workload; that can be throttled (or delayed) or are for anHA pair (jointly referred to as workload signature). This informationagain is obtained from the various counters that are maintained by thestorage operating system 107. The service time for the workload mix iscomputed and maybe referred to as workload mix signature.

In block B624, historical service times for the monitored resources aresearched to determine if the workload mix signature is within a certainpercentage (X %), for example, within 10%. This is performed by theanalysis module 223.

In block B626A, if the service time of the workload mix is within acertain percentage (X %), for example, 10% of the service time of theresource for which latency/utilization data has been collected, then theoperational point may be modified by adding or reducing the utilizationvalue of the resource.

If the service time is beyond X %, then a new optimal point maybecalculated based on a modified workload mix in block B626B. The modifiedworkload mix (i.e. a new actual workload mix) is based on the servicetime of the workload mix from block B622 with portions of the workloadthat is added or removed. Historical service time values are againsearched for observations to modify the workload mix. The actualheadroom is the difference between the new actual optimal point and thenew operational point, as shown in FIG. 4D and described above.

In one aspect, the analysis module 223 validates the operational pointvalues and their significance. For example, the analysis module 223validates the operational point based on neighboring values, removesoutliers, and marks any events that may affect the validity of theoperational points.

In one aspect, analysis module 223 looks at “back to back” consistencypoints for adjusting workload mix. Typically, CP operations areconducted in the background and are given lower priority, but if the CPbecomes a high priority, then the optimal point is calculated by lookingat the CP traffic.

In another aspect, analysis module 223 evaluates single threadedbehavior where the workloads access very few volumes. As a result, theLvU curves are distorted because high latencies may be observed acrossmultiple node processors. In such a case, headroom values may beinvalidated.

In one aspect, the observation based technique is based on selection ofobservations, interpolation between the observations and extrapolationbeyond what is observed for a resource. The observation based techniqueshas various advantages, for example, using historical data with currentdata provides a smooth LvU curve. The optimal points using workloadsignature mirrors real operating environments and provides an effectiveheadroom value.

In one aspect, methods and systems for managing resources in a networkedstorage environment are provided. One method is based on FIGS. 6A and 6Bdescribed above. The method includes generating a relationship betweenlatency and utilization of a resource in a networked storage environmentusing observation based, current and historical latency and utilizationdata, where latency is an indicator of delay at the resource forprocessing any request and utilization of the resource is an indicatorof an extent the resource is being used at any given time; and selectingan optimal point for the generated relationship between latency andutilization, where the optimal point is an indicator of resourceutilization beyond which throughput gains for a workload is smaller thanincrease in latency.

Seed Curves:

In one aspect, the LvU curve may be a pre-measured curve, called a seedcurve that is constructed in a laboratory environment. Seed curves maybe stored at a data structure by the performance manager 121. Seedcurves may be used when there are not enough observations to generate acurve. In one aspect, workload characteristics and the resources arematched with the resources and the workloads that were used to generatethe seed curve. The prediction strength of the seed curve would dependon how well the resources/workloads match the workloads and resourcesused in the laboratory setting.

In one aspect, the foregoing systems and techniques provide a mechanismto determine a resource's available capacity at any given time. Thisallows a user to optimize resource utilization and also enables thestorage system provider to meet contractual SLOs.

In one aspect, headroom is an efficient metric to determine performancecapacity of a resource. The metric can be efficiently used in systemswhere a plurality of resources serve complex workloads for storing data.

Storage System Node:

FIG. 7 is a block diagram of a node 208.1 that is illustrativelyembodied as a storage system comprising of a plurality of processors702A and 702B, a memory 704, a network adapter 710, a cluster accessadapter 712, a storage adapter 716 and local storage 713 interconnectedby a system bus 708. Node 208.1 is used as a resource and may be used toprovide node and storage utilization information to performance manager121 described above in detail.

Processors 702A-702B may be, or may include, one or more programmablegeneral-purpose or special-purpose microprocessors, digital signalprocessors (DSPs), programmable controllers, application specificintegrated circuits (ASICs), programmable logic devices (PLDs), or thelike, or a combination of such hardware devices. Idle time forprocessors 702A-702A is tracked by counters 306A, described above indetail.

The local storage 713 comprises one or more storage devices utilized bythe node to locally store configuration information for example, in aconfiguration data structure 714. The configuration information mayinclude information regarding storage volumes and the QOS associatedwith each storage volume.

The cluster access adapter 712 comprises a plurality of ports adapted tocouple node 208.1 to other nodes of cluster 202. In the illustrativeaspect, Ethernet may be used as the clustering protocol and interconnectmedia, although it will be apparent to those skilled in the art thatother types of protocols and interconnects may be utilized within thecluster architecture described herein. In alternate aspects where thenetwork modules and storage modules are implemented on separate storagesystems or computers, the cluster access adapter 712 is utilized by thenetwork/storage module for communicating with othernetwork/storage-modules in the cluster 202.

Each node 208.1 is illustratively embodied as a dual processor storagesystem executing a storage operating system 706 (similar to 107, FIG.1B) that preferably implements a high-level module, such as a filesystem, to logically organize the information as a hierarchicalstructure of named directories and files at storage 212.1. However, itwill be apparent to those of ordinary skill in the art that the node208.1 may alternatively comprise a single or more than two processorsystems. Illustratively, one processor 702A executes the functions ofthe network module on the node, while the other processor 702B executesthe functions of the storage module.

The memory 704 illustratively comprises storage locations that areaddressable by the processors and adapters for storing programmableinstructions and data structures. The processor and adapters may, inturn, comprise processing elements and/or logic circuitry configured toexecute the programmable instructions and manipulate the datastructures. It will be apparent to those skilled in the art that otherprocessing and memory means, including various computer readable media,may be used for storing and executing program instructions pertaining tothe disclosure described herein.

The storage operating system 706 portions of which is typically residentin memory and executed by the processing elements, functionallyorganizes the node 208.1 by, inter alia, invoking storage operation insupport of the storage service implemented by the node.

In one aspect, data that needs to be written is first stored at a bufferlocation of memory 704. Once the buffer is written, the storageoperating system acknowledges the write request. The written data ismoved to NVRAM storage and then stored persistently.

The network adapter 710 comprises a plurality of ports adapted to couplethe node 208.1 to one or more clients 204.1/204.N over point-to-pointlinks, wide area networks, virtual private networks implemented over apublic network (Internet) or a shared local area network. The networkadapter 710 thus may comprise the mechanical, electrical and signalingcircuitry needed to connect the node to the network. Each client204.1/204.N may communicate with the node over network 206 (FIG. 2A) byexchanging discrete frames or packets of data according to pre-definedprotocols, such as TCP/IP.

The storage adapter 716 cooperates with the storage operating system 706executing on the node 208.1 to access information requested by theclients. The information may be stored on any type of attached array ofwritable storage device media such as video tape, optical, DVD, magnetictape, bubble memory, electronic random access memory, micro-electromechanical and any other similar media adapted to store information,including data and parity information. However, as illustrativelydescribed herein, the information is preferably stored at storage device212.1. The storage adapter 716 comprises a plurality of ports havinginput/output (I/O) interface circuitry that couples to the storagedevices over an I/O interconnect arrangement, such as a conventionalhigh-performance, Fibre Channel link topology.

Operating System:

FIG. 8 illustrates a generic example of storage operating system 706 (or107, FIG. 1B) executed by node 208.1, according to one aspect of thepresent disclosure. The storage operating system 706 interfaces with theQOS module 109 and the performance manager 121 such that properbandwidth and QOS policies are implemented at the storage volume level.The storage operating system 706 may also maintain a plurality ofcounters for tracking node utilization and storage device utilizationinformation. For example, counters 306A-306B and 316A-316C may also bemaintained by the storage operating system 706 and counter informationis provided to the performance manager 121. In another aspect,performance manager 121 maintains the counters and they are updatedbased on information provided by the storage operating system 706.

In one example, storage operating system 706 may include severalmodules, or “layers” executed by one or both of network module 214 andstorage module 216. These layers include a file system manager 800 thatkeeps track of a directory structure (hierarchy) of the data stored instorage devices and manages read/write operation, i.e. executesread/write operation on storage in response to client 204.1/204.Nrequests.

Storage operating system 706 may also include a protocol layer 802 andan associated network access layer 806, to allow node 208.1 tocommunicate over a network with other systems, such as clients204.1/204.N. Protocol layer 802 may implement one or more of varioushigher-level network protocols, such as NFS, CIFS, Hypertext TransferProtocol (HTTP), TCP/IP and others.

Network access layer 806 may include one or more drivers, whichimplement one or more lower-level protocols to communicate over thenetwork, such as Ethernet. Interactions between clients' and massstorage devices 212.1-212.3 (or 114) are illustrated schematically as apath, which illustrates the flow of data through storage operatingsystem 706.

The storage operating system 706 may also include a storage access layer804 and an associated storage driver layer 808 to allow storage module216 to communicate with a storage device. The storage access layer 804may implement a higher-level storage protocol, such as RAID (redundantarray of inexpensive disks), while the storage driver layer 808 mayimplement a lower-level storage device access protocol, such as FibreChannel or SCSI. The storage driver layer 808 may maintain various datastructures (not shown) for storing information regarding storage volume,aggregate and various storage devices.

As used herein, the term “storage operating system” generally refers tothe computer-executable code operable on a computer to perform a storagefunction that manages data access and may, in the case of a node 208.1,implement data access semantics of a general purpose operating system.The storage operating system can also be implemented as a microkernel,an application program operating over a general-purpose operatingsystem, such as UNIX® or Windows XP®, or as a general-purpose operatingsystem with configurable functionality, which is configured for storageapplications as described herein.

In addition, it will be understood to those skilled in the art that thedisclosure described herein may apply to any type of special-purpose(e.g., file server, filer or storage serving appliance) orgeneral-purpose computer, including a standalone computer or portionthereof, embodied as or including a storage system. Moreover, theteachings of this disclosure can be adapted to a variety of storagesystem architectures including, but not limited to, a network-attachedstorage environment, a storage area network and a storage devicedirectly-attached to a client or host computer. The term “storagesystem” should therefore be taken broadly to include such arrangementsin addition to any subsystems configured to perform a storage functionand associated with other equipment or systems. It should be noted thatwhile this description is written in terms of a write any where filesystem, the teachings of the present disclosure may be utilized with anysuitable file system, including a write in place file system.

Processing System:

FIG. 9 is a high-level block diagram showing an example of thearchitecture of a processing system 900 that may be used according toone aspect. The processing system 900 can represent performance manager121, host system 102, management console 118, clients 116, 204, orstorage system 108. Note that certain standard and well-known componentswhich are not germane to the present aspects are not shown in FIG. 9.

The processing system 900 includes one or more processor(s) 902 andmemory 904, coupled to a bus system 905. The bus system 905 shown inFIG. 9 is an abstraction that represents any one or more separatephysical buses and/or point-to-point connections, connected byappropriate bridges, adapters and/or controllers. The bus system 905,therefore, may include, for example, a system bus, a PeripheralComponent Interconnect (PCI) bus, a HyperTransport or industry standardarchitecture (ISA) bus, a small computer system interface (SCSI) bus, auniversal serial bus (USB), or an Institute of Electrical andElectronics Engineers (IEEE) standard 1394 bus (sometimes referred to as“Firewire”).

The processor(s) 902 are the central processing units (CPUs) of theprocessing system 900 and, thus, control its overall operation. Incertain aspects, the processors 902 accomplish this by executingsoftware stored in memory 904. A processor 902 may be, or may include,one or more programmable general-purpose or special-purposemicroprocessors, digital signal processors (DSPs), programmablecontrollers, application specific integrated circuits (ASICs),programmable logic devices (PLDs), or the like, or a combination of suchdevices.

Memory 904 represents any form of random access memory (RAM), read-onlymemory (ROM), flash memory, or the like, or a combination of suchdevices. Memory 904 includes the main memory of the processing system900. Instructions 906 implement the process steps of FIGS. 4A, 5 and 6described above may reside in and executed by processors 902 from memory904.

Also connected to the processors 902 through the bus system 905 are oneor more internal mass storage devices 910, and a network adapter 912.Internal mass storage devices 910 may be, or may include anyconventional medium for storing large volumes of data in a non-volatilemanner, such as one or more magnetic or optical based disks. The networkadapter 912 provides the processing system 900 with the ability tocommunicate with remote devices (e.g., storage servers) over a networkand may be, for example, an Ethernet adapter, a Fibre Channel adapter,or the like.

The processing system 900 also includes one or more input/output (I/O)devices 908 coupled to the bus system 905. The I/O devices 908 mayinclude, for example, a display device, a keyboard, a mouse, etc.

Cloud Computing:

The system and techniques described above are applicable and especiallyuseful in the cloud computing environment where storage is presented andshared across different platforms. Cloud computing means computingcapability that provides an abstraction between the computing resourceand its underlying technical architecture (e.g., servers, storage,networks), enabling convenient, on-demand network access to a sharedpool of configurable computing resources that can be rapidly provisionedand released with minimal management effort or service providerinteraction. The term “cloud” is intended to refer to a network, forexample, the Internet and cloud computing allows shared resources, forexample, software and information to be available, on-demand, like apublic utility.

Typical cloud computing providers deliver common business applicationsonline which are accessed from another web service or software like aweb browser, while the software and data are stored remotely on servers.The cloud computing architecture uses a layered approach for providingapplication services. A first layer is an application layer that isexecuted at client computers. In this example, the application allows aclient to access storage via a cloud.

After the application layer, is a cloud platform and cloudinfrastructure, followed by a “server” layer that includes hardware andcomputer software designed for cloud specific services. The storagesystems/performance manager described above can be a part of the serverlayer for providing storage services. Details regarding these layers arenot germane to the inventive aspects.

Thus, methods and apparatus for managing resources in a storageenvironment have been described. Note that references throughout thisspecification to “one aspect” or “an aspect” mean that a particularfeature, structure or characteristic described in connection with theaspect is included in at least one aspect of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an aspect” or “one aspect” or “an alternative aspect” invarious portions of this specification are not necessarily all referringto the same aspect. Furthermore, the particular features, structures orcharacteristics being referred to may be combined as suitable in one ormore aspects of the disclosure, as will be recognized by those ofordinary skill in the art.

While the present disclosure is described above with respect to what iscurrently considered its preferred aspects, it is to be understood thatthe disclosure is not limited to that described above. To the contrary,the disclosure is intended to cover various modifications and equivalentarrangements within the spirit and scope of the appended claims.

What is claimed is:
 1. A machine implemented method, comprising:generating a relationship between latency and utilization of a resourcein a networked storage environment using observation based, current andhistorical latency and utilization data, where latency is an indicatorof delay at the resource for processing any request and utilization ofthe resource is an indicator of an extent the resource is being used atany given time; and selecting an optimal point for the generatedrelationship between latency and utilization, where the optimal point isan indicator of resource utilization beyond which throughput gains for aworkload is smaller than increase in latency.
 2. The machine implementedmethod of claim 1, wherein the resource is a processor for a networkingmodule that provides networking functionality in the networked storageenvironment and a processor for a storage module that interfaces with astorage device for storing data.
 3. The machine implemented method ofclaim 2, wherein the resource is an aggregate that includes the storagedevice.
 4. The machine implemented method of claim 1, wherein historicallatency data is used to interpolate any missing data from the currentlatency data.
 5. The machine implemented method of claim 1, wherein whenan incomplete curve between latency and utilization is generated basedon current and historical latency and utilization data, then theincomplete curve is completed by extrapolation.
 6. The machineimplemented method of claim 1, wherein available performance capacity ofthe resource is based on the optimal point and an operational point thatis determined after removing any outlier value from the current latencyand utilization data.
 7. The machine implemented method of claim 1,wherein to generate the latency and utilization relationship, a servicetime for the plurality of requests is used to search for historicallatency and utilization data for augmenting the current latency andutilization data.
 8. A non-transitory, machine readable storage mediumhaving stored thereon instructions for performing a method, comprisingmachine executable code which when executed by at least one machine,causes the machine to: generate a relationship between latency andutilization of a resource in a networked storage environment usingobservation based, current and historical latency and utilization data,where latency is an indicator of delay at the resource for processingany request and utilization of the resource is an indicator of an extentthe resource is being used at any given time; and select an optimalpoint for the generated relationship between latency and utilization,where the optimal point is an indicator of resource utilization beyondwhich throughput gains for a workload is smaller than increase inlatency.
 9. The storage medium of claim 8, wherein the resource is aprocessor for a networking module that provides networking functionalityin the networked storage environment and a processor for a storagemodule that interfaces with a storage device for storing data.
 10. Thestorage medium of claim 9, wherein the resource is an aggregate thatincludes the storage device.
 11. The storage medium of claim 8, whereinhistorical latency data is used to interpolate any missing data forcurrent latency data.
 12. The storage medium of claim 8, wherein when anincomplete curve between latency and utilization is generated based oncurrent and historical latency and utilization data, then the incompletecurve is completed by extrapolation.
 13. The storage medium of claim 8,wherein available performance capacity of the resource is based on theoptimal point and an operational point that is determined after removingany outlier value from the current latency and utilization data.
 14. Thestorage medium of claim 8, wherein to generate the latency andutilization relationship, a service time for the plurality of requestsis used to search for historical latency and utilization data foraugmenting the current latency and utilization data.
 15. A systemcomprising: a memory containing machine readable medium comprisingmachine executable code having stored thereon instructions; and aprocessor module coupled to the memory, the processor module configuredto execute the machine executable code to: generate a relationshipbetween latency and utilization of a resource in a networked storageenvironment using observation based, current and historical latency andutilization data, where latency is an indicator of delay at the resourcefor processing any request and utilization of the resource is anindicator of an extent the resource is being used at any given time; andselect an optimal point for the generated relationship between latencyand utilization, where the optimal point is an indicator of resourceutilization beyond which throughput gains for a workload is smaller thanincrease in latency.
 16. The system of claim 15, wherein the resource isa processor for a networking module that provides networkingfunctionality in the networked storage environment and a processor for astorage module that interfaces with a storage device for storing data.17. The system of claim 16, wherein the resource is an aggregate thatincludes the storage device.
 18. The system of claim 15, whereinhistorical latency data is used to interpolate any missing data forcurrent latency data.
 19. The system of claim 15, wherein when anincomplete curve between latency and utilization is generated based oncurrent and historical latency and utilization data, then the incompletecurve is completed by extrapolation.
 20. The system of claim 15, whereinavailable performance capacity of the resource is based on the optimalpoint and an operational point that is determined after removing anyoutlier value from the current latency and utilization data.
 21. Thesystem of claim 15, wherein to generate the latency and utilizationrelationship, a service time for the plurality of requests is used tosearch for historical latency and utilization data for augmenting thecurrent latency and utilization data.